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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Robot. AI | doi: 10.3389/frobt.2019.00120

Estimation of User-Applied Isometric Force/Torque using Upper Extremity Force Myography

 Maram Sakr1, 2, Xianta Jiang1, 3 and  Carlo Menon1*
  • 1Simon Fraser University, Canada
  • 2University of British Columbia, Canada
  • 3Memorial University of Newfoundland, Canada

Hand force estimation is critical for applications that involve physical human-machine interactions for force monitoring and machine control. Force Myography (FMG) is a potential technique to be used for estimating hand force/torque. The FMG signals reflect the volumetric changes in the arm muscles due to muscle contraction or expansion. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm to measure the FMG signals for isometric force/torque estimation. Nine participants were recruited in this study and were asked to exert isometric force along three perpendicular axes, torque about the same three axes, and force and torque simultaneously. During the tests, the isometric force and torque were measured using a 6-degree-of-freedom (DOF) (i.e. force in three axes and torque around the same axes) load cell for ground truth labels whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the different locations of the arm. A two-stage regression strategy was employed to enhance the performance of the FMG bands, where three regression algorithms including support vector regression (SVR), general regression neural network (GRNN), and random forest regression (RF) models were employed respectively in the first stage and GRNN was used in the second stage. Two cases were considered to explore the performance of the FMG bands in estimating: (1) 3-DOF force and 3-DOF torque at once and (2) 6-DOF force and torque. In addition, the impact of sensors placement and the spatial coverage of FMG measurements were studied. This preliminary investigation demonstrates promising potential of FMG to estimate multi-DOF isometric force/torque. Specifically, R2 accuracies of 0.83 for the 3-axis force, 0.84 for 3-axis torque, and 0.77 for the combination of force and torque (6-DOF) regressions were obtained using the four bands on the arm in cross-trial evaluation.

Keywords: Hand force/torque estimation, human-machine interaction, force myography, wearable sensors, Multi-output regression

Received: 17 Jun 2019; Accepted: 01 Nov 2019.

Copyright: © 2019 Sakr, Jiang and Menon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Carlo Menon, Simon Fraser University, Burnaby, Canada, cmenon@sfu.ca